Knowledge and Usage of Cervical Cancer Screening for Cancer Prevention by Reproductive Age Women
Bibliographic record
Abstract
Cervical cancer –CCa screening is an effective measure mapped out for preventing cancer occurrences in women. This study determined the status of knowledge and usage of CCa screening for cancer prevention among reproductive age Nigerian women. The study was based on descriptive research design and was conducted in Nigeria, from October 2017 to April 2018 and comprised of reproductive age women. The participants, aged 15-49 years, were vulnerable to CCa. The instrument used for data collection was structured questionnaire. Statistical Package for Social Science version 21 was used for data analysis. All the participants were Nigerians totalling 1300. Of all, 1249(96.1%) completed the questionnaire correctly. Majority of the participants were: Christians 825(66.0%), Single 695(55.6%), aged 26-35 years 673(53.8%), and had Secondary education 753(60.2%). A greater proportion of the participants 1073(86.7%) knew about CCa screening. Among them, only 513(41.7%) were screened. The status of knowledge and usage of CCa screening varied within variables. A statistically significant difference was observed with regards to Age by birth (P-value <0.05) while none existed on marital status, religion and educational level (P-value >0.05). There is obvious gap between what is known about CCa screening service for cancer prevention and the actual usage by the women. The majority of the women knew about the available services but only few of them had used it. This implies that there is obvious imbalance between the quality of knowledge of a given health service and the actual usage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".